Rainmaker Fellow, Machine Learning - Rainmaker Technology

Examples of the Work

Fellowship projects change with Rainmaker's research and operational priorities. Examples of the work our ML team may pursue include:

• Developing a short-range supercooled liquid water opportunity forecast using public NWP and Rainmaker observations.
• Predicting hail-core growth, motion, splitting, and decay from radar sequences.
• Building a bounded multimodal atmospheric-state reconstruction pilot.
• Improving microwave-sounder retrievals using Rainmaker observations.
• Modeling another scientific or operational problem selected with Rainmaker's ML and atmospheric-science teams.

What You'll Do

• Translate a scientific or operational question into a measurable ML problem.
• Build or improve the training and validation dataset needed for the project.
• Establish simple, reproducible baselines before introducing more complex models.
• Train, evaluate, and debug models using held-out weather events, regions, or operating conditions.
• Quantify calibration, uncertainty, generalization, failure modes, and sensitivity to missing or biased data.
• Work closely with atmospheric scientists to define useful targets, ground truth, physical constraints, and operational success criteria.
• Produce clear, reusable code and documentation.
• Present your results to Rainmaker's scientists, engineers, operators, and technical leadership.
• Deliver a final artifact such as a benchmark dataset, model, prototype product, evaluation report, or research paper.

What We're Looking For

• Current undergraduate, master's, or PhD students; postdoctoral researchers; recent graduates; and other early-career researchers are all eligible.
• Strong Python programming ability and experience with a modern ML framework.
• Evidence that you can independently build, test, and debug technical work.
• Strong quantitative reasoning and an ability to design credible experiments.
• Interest in noisy, sparse, multimodal, spatial, temporal, or physical data.
• Ability to make progress on ambiguous research problems while incorporating mentor feedback.
• Clear written and verbal communication.
• Availability for full-time, on-site work in El Segundo for the agreed appointment.

Particularly Relevant Backgrounds

• Machine learning, computer science, applied mathematics, statistics, physics, meteorology, remote sensing, robotics, autonomy, geospatial analysis, or scientific computing.
• Forecasting, sequence modeling, computer vision, state estimation, sensor fusion, probabilistic modeling, data assimilation, or uncertainty quantification.
• Weather knowledge is valuable but not required.

What Success Looks Like